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Is there any genetic association between oestrogen receptor alpha [ERα]-PvuII polymorphism and idiopathic male infertility?
Design
A total of 226 infertile and 213 fertile men participated in the present case-control study. ERα-PvuII genotyping was performed using the polymerase chain reaction-restriction fragment length polymorphism [PCR-RFLP] method. Meta-analysis was also performed by pooling data collected from seven other eligible studies identified by searches of PubMed, Embase, Google Scholar, and Science Direct databases. Summary odds ratios were estimated by fixed- or random-effects models. The molecular effects of ERα-PvuII polymorphism were evaluated by bioinformatics tools.
Results
A significant protective association was reported between ERα-PvuII and male infertility in the homozygote model [OR=0.54, 95%CI=0.3–0.98, p=0.042]. Also, a similar association was observed in asthenozoospermia subgroup [OR=0.4, 95%CI=0.18–0.9, p=0.025]. Meta-analysis also revealed that the ER-PvuII polymorphism was significantly associated with the decreased risk of male infertility in the heterozygote co-dominant model [OR=0.80, 95%CI=0.64–0.99, p=0.042]. Moreover, similar protective results were reported in stratified analyses in Caucasian subgroup in the dominant genetic model [OR=0.66, 95%CI=0.45–0.96, p=0.029] and in the heterozygote co-dominant model [OR=0.62, 95%CI=0.41–0.93, p=0.021]. A significant association was also found in studies with sample size of less than 400 subjects in heterozygote co-dominant model [OR=0.69, 95%CI=0.50–0.95, p=0.023]. The bioinformatics data indicated that ER-PvuII polymorphism could significantly affect RNA structure of ERα [p=0.004].
Conclusion
The ERα-PvuII polymorphism could be considered as a possible protective factor against male infertility.
]. Environmental or genetic factors affect the male reproductive system. It can be noted that genetic factors are the most important causes of male infertility [
]. Genetic polymorphisms of key genes may contribute to male infertility. Mutations in oestrogen receptor genes are other important factors increasing the risk of male infertility [
Oestrogen, which is traditionally known as a female-specific hormone, is now considered as comprising a group of hormones playing an important role in the growth and amplification of both female and male reproductive systems [
]. Oestrone (E1), 17-beta oestradiol (E2) and oestriol (E3) are three types of oestrogens that are produced by the human body, of which 17-beta oestradiol is the most active form [Yashwanth et al., 2006]. In the testis, oestrogen is produced by Sertoli, Leydig, and germ cells, and plays roles in sperm maturation and capacitation, and the acrosome reaction. In addition, it supports tight junctions between cells of testis tissues [
Oestrogen action is mainly mediated by both ERα and ERβ receptors. ERα or ER1 gene is located on chromosome 6 [6q25] and encodes a protein with 595 residues. ERβ or ER2 is another type of oestrogen receptor including 530 residues and it is encoded by a gene on chromosome 14 [14q22-24] [
]. The PvuII [c.453-397T>C, with ID: rs2234693] is a widely studied polymorphism in ERα and occurs by the substitution of C with T nucleotide in the intron 4 of ERα gene [
Association of oestrogen receptor α polymorphisms and androgen receptor CAG trinucleotide repeats with male infertility: a study in 109 Greek infertile men.
]. Therefore, the current study aimed to investigate the association between ERα-PvuII polymorphism and male infertility in an Iranian population following by a meta-analysis with an in silico approach.
Materials and methods
Subjects
Two hundred twenty-six men with idiopathic infertility and 213 healthy men without any history of infertility participated in the analysis of the ERα-PvuII polymorphism. The reproduction clinic of Shahid Beheshti hospital affiliated to Kashan University of Medical Sciences [Kashan, Iran] was the referral center for patient selection. Infertile men had no history of systemic and genetic diseases and were identified through comprehensive interviews. Infertile men with orchitis, maldescent of testis, varicocele, obstruction of vas deferens, immune or infectious abnormalities, drug abuse, diabetes mellitus, abnormal hormone profile [LH, FSH, and testosterone], abnormal karyotype, and Y-chromosome micro-deletions were excluded from the research. According to the spermiogram, infertile men were categorized into oligozoospermia, asthenozoospermia, teratozoospermia, and non-obstructive azoospermia [NOA] subgroups [
]. The control group was randomly selected from fertile men with normal sperm parameters referring to the same clinic with at least one child conceived naturally. All subjects with genetic and familial diseases were also excluded. Since the ERα-PvuII polymorphism is a genetic risk factor for various diseases such as hepatocellular carcinoma [
], cases and controls were screened for the aforementioned diseases with the help of specialist physicians. All subjects signed written informed consent forms; the research was approved by the Medical Research Ethics Committee of Kashan University of Medical Sciences [Reference no. IR.KAUMS.REC.1396.24] on 17th May 2017.
SNP genotyping
Intravenous blood samples were taken from all participants and poured into tubes containing EDTAk+ and preserved at -20°C. DynaBioTM genomic blood DNA extraction kit [Takapouzist Co., Tehran, Iran] was used according to manufacturer’s recommendations in order to isolate genomic DNA from peripheral blood samples and products were stored at -20°C until analysis. The polymerase chain reaction-restriction fragment length polymorphism [PCR-RFLP] technique was utilized for detecting genotypes in PvuII region. Forward and reverse primers were designed around the PvuII polymorphic region of ERα gene by using Oligo7 software. Table 1 presents sequences of forward and reverse primers. PCR was performed in a total volume of 20µl containing 10µl of 2X Taq PreMix, 0.35µM of each of forward and reverse primers and 50ng of extracted DNA template. A thermocycling protocol was performed, including initial denaturation at 94°C for 5 min followed by 35 repetitive cycles consisting of a denaturation step at 94°C for 45 seconds, annealing step at 62.5°C for 45 seconds, extension step at 72°C for 45 seconds, and final extension at 72°C for 5 min in the peqSTAR thermal cycler [PeqLab, Erlangen, Germany]. All PCR reagents were purchased from CinaGen Company [Tehran, Iran]. A 369-bp fragment of ERα was detected after amplification by specific primers. Amplified fragments were subsequently digested by PvuII restriction enzyme [Fermentas Co., Sankt Leon-Rot, Germany] according to company protocol. Digested products were separated by electrophoresis on 2.0% agarose gel and visualized by DNA safe staining protocol [DNA Green viewer, Parstous Co., Tehran Iran]. Table 1 presents the predicted model of genotypes. Randomly selected DNA samples from CC, CT, and TT genotypes were sequenced by Bioneer Company [Daejeon, South Korea] in order to confirm the PCR-RFLP procedure. Digestion of 369-bp fragment by PvuII enzyme revealed that subjects with TT genotype created 289- and 80-bp fragments. In samples with CT genotypes, 369-, 289-, and 80-bp fragments were created while samples with CC genotype had no restriction site for PvuII enzyme [Figure 1].
Table 1Primers sequences and genotyping conditions
Figure 1ERα gene map, PCR_RFLP and DNA sequencing results: [A] Human ERα map was obtained from NCBI with 22 exons, and PvuII [rs2234693] SNP is shown in intron 4; [B] Restriction enzyme map: PvuII restriction enzyme of 369-bp PCR fragment in 1% agarose gel [lane M=100-bp DNA marker; lane 1=CC genotype; lane 2=CT genotype; lane 3=TT genotype]; [C] partial sequences of ERα at the rs2234693 position.
The meta-analysis is in accordance with the PRISMA checklist and included relevant studies on the association between ERα-PvuII polymorphism and male infertility. A widespread systematic search was conducted for published literature in medical databases including PubMed, Embase, Google Scholar, and Science Direct without any restriction on languages until November 2017 in order to find all papers containing the aforementioned genetic association study. The search was carried out by the following keywords: “male infertility or NOA or oligozoospermia or oligoasthenoteratozoospermia or sub infertility” and “oestrogen receptors or ER” and “polymorphism or SNP or variation” and “PvuII or rs2234693”. Subsequently, all references of relevant papers found in primary computer searches were checked. The inclusion criteria for the meta-analysis were: (i) Studies on humans; (ii) Studies with case-control designs; (iii) Studies on the association between ERI-PvuII polymorphism and male infertility; (iv) Studies containing adequate data about genotype frequencies to estimate odds ratio [OR] at 95% confidence interval [CI]. Two authors [MK and NM] extracted all data individually meeting the inclusion criteria. The main data of eligible studies included last name of the first author, publication year, ethnicity, genotyping method, source of controls, sample size, and p-value of Hardy Weinberg Equilibrium [HWE] in the control group.
The quality of included papers was independently evaluated by 2 authors (MK and NM) based on the STREGA (STrengthening the REporting of Genetic Association Studies) principle (
). The STREGA system includes 22 quality assessment evaluations with scores of 0–22. The included studies were categorized into three levels based on their scores: low quality (0–12), moderate to high quality (13–17), and high quality (18–22). Disagreements on the aforementioned scores of the included studies in our meta-analysis were solved via a comprehensive reconsideration by the authors. Also, GRADEprofiler software (version 3.6.1; Cochrane Collaboration) was utilized to evaluate the quality of studies according to the risk of bias, inconsistency of results in different studies, indirectness of evidence, imprecision of results, and publication bias.
Statistical analysis
A chi-squared test was utilized to determine the status of Hardy–Weinberg equilibrium. The same test was also used to evaluate differences of genotypes and frequencies of alleles between case and control groups. Logistic regression analysis was performed to estimate odds ratios [ORs] at 95% confidence interval [CI] for evaluating the association between the PvuII polymorphism and male infertility risk. All numerical variables were also evaluated by an independent t-test. A p-value less than 0.05 was considered as the statistical significance level. The statistical software package of SPSS ver. 20 [SPSS Inc., IBM Corp Armonk, NY, USA] was used for data analysis.
In the meta-analysis, pooled ORs were calculated for the allele model [T vs. C], homozygote co-dominant model [TT vs. CC], heterozygote co-dominant model [CT vs. CC], dominant model [CT+TT vs. CC], and recessive model [TT vs. CC+CT]. Cochran’s Q-test and I2 statistic were used to estimate the heterogeneities. The fixed-effects model was used in the presence of any heterogeneity [Pheterogeneity> 0.1], otherwise random-effects models were used at higher confidence intervals [
]. Sensitivity analyses were performed to estimate stabilities of meta-analysis results. Funnel plots and Egger’s test were used to evaluate possible publication biases [
]. The Open Meta Analyst and Comprehensive Meta-Analysis software were used for all calculations in the meta-analysis.
In silico analysis
In the present study, some bioinformatics were utilized to evaluate effects of molecular aspects of PvuII polymorphism on ERα. To this end, the entire sequence of the ERα gene was initially extracted from National Center for Biotechnology Information (NCBI) database. The location of the PvuII polymorphism was determined on intron 4 of ERα gene. This substitution might thus affect the pre-mature RNA and/or RNA splicing procedure. The impact of PvuII substitution on the stability of RNA structure was first evaluated by the Mfold web server [
], were used to assess effects of aforementioned SNP on the splicing model of ERα.
Results
Characteristics of research population
Table 2 presents the demographic and sperm parameters of research subjects. There were no statistically significant differences of age, status of smoking, and body mass index [BMI] between infertile [mean age of 33.65±5.26, 31.42% having smoked, and mean BMI of 24.60±2.37] and fertile [mean age of 32.81±5.27, 39.44% having smoked, and mean BMI of 24.21±2.27] men. Table 2 also presents sperm parameters in detail.
Table 2Demographic and sperm parameters of the subjects
Variables
Fertile (n=213)
Infertile (n=226)
p-value
Age (Years)
32.81±5.27
33.65±5.26
NS
Smoking (Y/N)
84/129
71/155
NS
BMI (kg/m2)
24.21±2.27
24.60±2.37
NS
Semen volume (mL)
3.40±0.71
3.32±0.83
NS
Sperm count (× 106/mL)
60.35±11.77
24.27±19.60
< 0.0001
Motility (% motile)
56.29±10.50
28.49±22.11
< 0.0001
Morphology (% normal)
55.14±11.52
21.95±22.93
< 0.0001
The data are expressed as mean ± standard deviation. NS = not statistically significant.
Genotype frequencies of ER-PvuII polymorphism were calculated and statistical analysis indicated that distribution of all genotypes were consistent with the Hardy-Weinberg equilibrium [HWE] in both patient and control groups. Table 3 presents genotype and allele frequencies of the PvuII polymorphism in fertile and infertile groups. Frequencies of CC, CT, and TT genotypes were 19.47%, 52.65%, and 27.88% respectively for the patient group and 12.68%, 53.99%, and 33.33% respectively for the fertile group. C and T allele rates were 45.8% and 54.2% respectively for the infertile group and 39.67% and 60.33% respectively in the control group. As presented in Table 3, there were statistically significant protective associations between TT genotype and both total infertile group [OR=0.54, 95%CI=0.3–0.98, p=0.042] and asthenozoospermia subgroup [OR=0.4, 95%CI=0.18–0.9, p=0.025]. As a preliminary study, an analysis was performed to find any genetic associations of PvuII with infertile subgroups. Significant protective associations were identified between the ER-PvuII polymorphism and both asthenozoospermia [OR=0.47, 95%CI=0.24–0.92, p=0.029] and NOA [OR=0.46, 95%CI=0.21–0.99, p=0.046] in a dominant genetic model. The allele analysis indicated a significant protective association between the presence of the T allele and asthenozoospermia [OR=0.68, 95%CI=0.46–0.99, p=0.044].
Table 3Association results in the case-control study
No. and Percentage
OR (95% CI)
p-value
Genotype
Control (n=213)
All cases (n=226)
Astheno (n=72)
Oligo (n=46)
Terato (n=58)
NOA (n=50)
Total
Astheno
Oligo
Terato
NOA
Total
Astheno
Oligo
Terato
NOA
CC
27 (12.68%)
44 (19.47%)
17 (23.61%)
9 (19.56%)
6 (10.35%)
12 (24.00%)
–
–
–
–
–
–
–
–
–
–
CT
115 (53.99%)
119 (52.65%)
37 (51.39%)
25 (54.35%)
32 (55.17%)
25 (50.00%)
0.64 (0.37–1.09)
0.51 (0.25–1.04)
0.65 (0.27–1.56)
1.25 (0.48–3.29)
0.49 (0.22–1.1)
NS
NS
NS
NS
NS
TT
71 (33.33%)
63 (27.88%)
18 (25%)
12 (26.09%)
20 (34.48%)
13 (26.00%)
0.54 (0.30–0.98)
0.40 (0.18–0.90)
0.51 (0.19–1.34)
1.27 (0.46–3.50)
0.41 (0.17–1.01)
0.042
0.025
NS
NS
NS
CT+TT
186 (87.32%)
182 (80.53%)
55 (76.39%)
37 (80.43%)
52 (89.66%)
38 (76.00%)
0.60 (0.36–1.01)
0.47 (0.24–0.92)
0.60 (0.26–1.37)
1.26 (0.49–3.21)
0.46 (0.21–0.99)
NS
0.029
NS
NS
0.046
C
169 (39.67%)
207 (45.8%)
71 (49.31%)
43 (46.74%)
44 (37.93%)
49 (49.00%)
–
–
–
–
–
–
–
–
–
–
T
257 (60.33%)
245 (54.2%)
73 (50.69%)
49 (53.26%)
72 (62.07%)
51 (51.00%)
0.78 (0.60–1.02)
0.68 (0.46–0.99)
0.75 (0.48–1.18)
1.08 (0.71–1.64)
0.68 (0.44–1.06)
NS
0.044
NS
NS
NS
OR: Odds ratio; CI: Confidence interval; Astheno: Asthenospermia; Oligo: Oligozoospermia; NOA: Non-obstructive azoospermia; Terato: Teratozoospermia; Significant differences between the case and control groups are bolded.
Figure 2 presents the flowchart of paper selection. 1092 papers were collected by the initial search according to the above-mentioned keywords. New papers were not found from references of collected studies. Among the 1092 papers, 871 were excluded because they did not meet inclusion criteria or were duplicates. Following the screening, 36 studies were excluded because they were meta-analyses or reviews. One study was also excluded because the required data were not available [
Association of oestrogen receptor α polymorphisms and androgen receptor CAG trinucleotide repeats with male infertility: a study in 109 Greek infertile men.
] and our data of the present study was added to the meta-analysis. Table 4 presents frequencies of alleles and genotypes along with ethnicity, genotyping methods, and sample size of each study. Among studies, four projects were performed in Caucasians, three studies belonged to Asian and one research study on a Brazilian population. According to STREGA scores, the quality scores for 5 studies were estimated moderate to high and for 3 studies were estimated high (Table 4). A GRADEprofiler assessment indicated that the quality of evidence was moderate in the heterozygote co-dominant and dominant models and low in allelic, homozygote co-dominant, and recessive models [Supplementary Table ].
Figure 2Flow diagram of the research selection process.
Association of oestrogen receptor α polymorphisms and androgen receptor CAG trinucleotide repeats with male infertility: a study in 109 Greek infertile men.
HWE, Hardy-Weinberg equilibrium; PCR, Polymerase chain reaction; RFLP, Restriction fragment length polymorphism. STREGA, STrengthening the REporting of Genetic Association studies. Genotype frequencies were in Hardy-Weinberg equilibrium for all eight studies
The meta-analysis was first performed on all subjects, and then stratified according to the ethnicity, genotyping methods, and sample size. Table 5 presents association results in details. As shown in Figure 3, when all 8 papers on 1066 patients and 1149 healthy controls were evaluated in the meta-analysis, results indicated that the ER-PvuII transition was significantly associated with the decreased risk of male infertility in CT vs. CC genetic model [OR=0.80, 95%CI=0.64–0.99, p=0.042]. The subgroup analysis based on the ethnicity revealed a significant protective association between the ER-PvuII polymorphism and male infertility in Caucasian subjects in a dominant genetic model [OR=0.66, 95%CI=0.45–0.96, p=0.029] and in CT vs. CC [OR=0.62, 95%CI=0.41–0.93, p=0.021]. An association were also found between the above-mentioned SNP and male infertility in studies on a sample size of less than 400 subjects in CT vs. CC genetic model [OR=0.69, 95%CI=0.50–0.95, p=0.023].
Table 5Association results in the meta-analysis.
Group
T vs. C
TT vs. CC
CT vs. CC
CT+TT vs. CC
TT vs. CC+CT
OR (95% CI)
p
OR (95% CI)
p
OR (95% CI)
p
OR (95% CI)
p
OR (95% CI)
p
Total
0.95 (0.79–1.16)
NS
0.90 (0.62–1.30)
NS
0.80 (0.64–0.99)
0.042
0.85 (0.66–1.04)
NS
1.07 (0.82–1.40)
NS
Ethnicity
Asian
1.08 (0.77–1.50)
NS
1.13 (0.55–2.34)
NS
0.88 (0.58–1.34)
NS
0.98 (0.58–1.63)
NS
1.21 (0.76–1.93)
NS
Caucasian
0.84 (0.62–1.13)
NS
0.71 (0.45–1.11)
NS
0.62 (0.41–0.93)
0.021
0.66 (0.45–0.96)
0.029
0.95 (0.61–1.49)
NS
Genotyping method
PCR-RFLP
0.93 (0.71–1.21)
NS
0.87 (0.52–1.46)
NS
0.80 (0.60–1.07)
NS
0.82 (0.57–1.19)
NS
1.03 (0.73–1.46)
NS
Real-time PCR
1 0.98 (0.78–1.23)
NS
0.91 (0.57–1.46)
NS
0.79 (0.53–1.17)
NS
0.84 (0.59–1.20)
NS
1.14 (0.73–1.80)
NS
Sample size
<400
0.92 (0.70–1.22)
NS
0.86 (0.53–1.40)
NS
0.69 (0.50–0.95)
0.023
0.78 (0.58–1.05)
NS
1.10 (0.73–1.67)
NS
>400
0.98 (0.72–1.34)
NS
0.94 (0.48–1.86)
NS
0.91 (0.61–1.34)
NS
0.93 (0.57–1.51)
NS
1.02 (0.69–1.50)
NS
OR: Odds ratio; CI: Confidence interval; PCR, Polymerase chain reaction; RFLP, Restriction fragment length polymorphism. Significant differences between the case and control groups are bolded.
Figure 3The forest plots of all selected studies on the association between ERα-PvuII polymorphism and male infertility risk under the co-dominant model [TT vs. CC], heterozygote co-dominant model [CT vs. CC], dominant model [CT+TT vs. CC], and recessive model [TT vs. CC+CT] in an overall analysis.
The heterogeneity test for the overall analyses suggested true heterogeneities in studies in T vs. C [Pheterogeneity=0.02, I2=56%], TT vs. CC [Pheterogeneity=0.04, I2=53%], TT vs. CC+CT [Pheterogeneity=0.09, I2=44%] genetic models [Table 6]. After stratified analyses according to the ethnicity, significant heterogeneities were observed between Asian studies in terms of T vs. C [Pheterogeneity=0.01, I2=76%], TT vs. CC [Pheterogeneity=0.01, I2=78%], CT+TT vs. CC [Pheterogeneity=0.05, I2=67%], TT vs. CC+CT [Pheterogeneity=0.03, I2=71%] genetic models. After stratified analyses by the genotyping method, there were significant heterogeneities between studies by RFLP method in terms of T vs. C [Pheterogeneity=0.01, I2=68%], TT vs. CC [Pheterogeneity=0.01, I2=66%], CT+TT vs. CC [Pheterogeneity=0.07, I2=51%], TT vs. CC+CT [Pheterogeneity=0.05, I2 = 55%] genetic models. Furthermore, significant heterogeneities were found in studies with sample size of more than 400 subjects in T vs. C [Pheterogeneity=0.02, I2=74%], TT vs. CC [Pheterogeneity=0.02, I2=75%], CT+TT vs. CC [Pheterogeneity=0.05, I2=67%], TT vs. CC+CT [Pheterogeneity=0.09, I2=58%] genetic models when the meta-analysis was stratified by sample size [Table 6].
Table 6Results of heterogeneity and publication bias in the meta-analysis
Group
T vs. C
TT vs. CC
CT vs. CC
CT+TT vs. CC
TT vs. CC+CT
Ph
I2
PE
Ph
I2
PE
Ph
I2
PE
Ph
I2
PE
Ph
I2
PE
Total
0.02
56%
NS
0.04
53%
NS
NS
0%
NS
NS
33%
NS
0.09
44%
NS
Ethnicity
Asian
0.01
76%
NS
0.01
78%
NS
NS
45%
NS
0.05
67%
NS
0.03
71%
NS
Caucasian
NS
38%
NS
NS
2%
NS
NS
0%
NS
NS
0%
NS
NS
31%
NS
Genotyping method
PCR-RFLP
0.01
68%
NS
0.01
66%
NS
NS
14%
NS
0.07
51%
NS
0.05
55%
NS
Real-time PCR
NS
0%
-
NS
0%
-
NS
5%
-
NS
0%
-
NS
25%
-
Sample size
<400
0.08
51%
NS
NS
41%
NS
NS
0%
NS
NS
0%
NS
NS
45%
NS
>400
0.02
74%
NS
0.02
75%
NS
NS
44%
NS
0.05
67%
NS
0.09
58%
NS
OR: Odds ratio; CI: Confidence interval; PCR, Polymerase chain reaction; RFLP, Restriction fragment length polymorphism; Ph: Pheterogeneity (p< 0.1 was considered as a significant difference). PE: PEgger (p< 0.05 was considered as a significant difference).
Begg’s funnel plot and Egger’s test were both performed to access the possible publication bias of this meta-analysis. Funnel plots with symmetric arrangements revealed that there was no publication bias in this meta-analysis [Figure 4]. P-values of Egger’s test indicated that there were no significant publication biases in any genetic models in overall and stratified analyses [Table 6]. Finally, the results of the sensitivity analysis showed no substantial impact of a single study on the overall results (data not shown).
Figure 4Funnel plot of standard error by log odds ratio for the association of ERα-PvuII polymorphism and male infertility in the overall population.
In silico analysis focused on the effects of PvuII transition on ERα pre-mature RNA structure and splicing procedure. Mfold software predicted eleven circular structure plots for a partial sequence of ERα containing PvuII polymorphism with particular Gibbs free energies (∆Gs) for RNA folding. The average ∆G was estimated as -78.18±1.16 and -79.78±1.16 kcal/mol for C and T alleles, respectively. Comparison of minimum free energies for alleles C and T indicated that there was a significant difference between two above-mentioned alleles [p=0.004]. In other words, the allele T could be more stable than allele C, but data from RNAsnp web server indicated that the mentioned SNP might not affect the splicing pattern of ERα. Data of NetGene2 also revealed that PvuII mutation could not alter the splicing pattern of ERα gene. In addition, SplicePort database revealed that the aforementioned polymorphism could change the score of donor and acceptor splicing junctions around the polymorphic position and that the changes were insignificant.
Discussion
The present study evaluated the association of ERα-PvuII gene polymorphism with idiopathic male infertility in an Iranian population followed by a meta-analysis. Our case-control study revealed that possession of the polymorphism with the TT genotype was a protective factor for the total infertile group and for subgroups. Moreover, our meta-analysis revealed that the ERα-PvuII polymorphism was a protective factor in the heterozygous co-dominant model in an overall analysis. The stratified analysis also indicated that this transition was a protective genetic factor in Caucasian populations. In addition, studies with sample sizes of less than 400 subjects had a lower risk for male infertility. However, significant heterogeneities were observed in overall analyses, but they disappeared in stratified analyses according to ethnicity, genotyping methods, and sample sizes. Therefore, these parameters might be sources of heterogeneities. It seems that some factors such as race and environmental factors may modulate the protective effects of the ERα-PvuII polymorphism. There was not any publication bias in the meta-analysis and the sensitivity analysis also revealed that our meta-analysis was reliable and robust.
Genetic variations such as single nucleotide polymorphisms can affect the gene expression and protein structure depending on their positions in genes. Evaluation of molecular effects of SNP by in vitro and in vivo methods is a time- and cost-consuming procedure; hence, computational methods are more beneficial than the above-mentioned methods [
]. For example, the gene expression is greatly dependent on the development of established structures by nucleotide pairing in the RNA. Creation of synonymous mutations to expose the start codon from secondary structures effectually optimized protein expression by 10-fold [
]. Therefore, the secondary structure of RNA can be largely independent of the tertiary structure. In the absence of any crystal structure, a comparative sequence analysis, in which a large number of sequences are aligned to detect the pairing pattern, is the gold standard for determining the secondary structure of RNA. When only a sequence is available, the free energy minimization can be used to predict the secondary structure [
]. The cellular activity of RNA normally depends on its specific structural features. This crucial structural role in functions of RNA molecules has led to the development of some software packages supporting the prediction of the secondary structure of RNA due to the difficulty in its laboratory determination [
]. The software includes computer programs based on the minimum free energy [MFE] algorithm. The MFE parameter of an RNA molecule is affected by three parameters of nucleotides in the corresponding sequence including numbers, composition, and arrangement of nucleotides. Long sequences are in fact relatively more stable because they can create more hydrogen bonds and more RNA stacking. Guanine and cytosine-rich RNAs are usually more stable than adenine and uracil-rich segments. The stability of RNA folding can be also affected by nucleotide sequences due to number and expansion of loops and double-helix conformations. Reports indicate that mRNAs and microRNA precursors have a more negative MFE than non-coding RNAs because of their nucleotide number and composition [
]. Therefore, the minimum free energy is a suitable criterion for evaluation of RNA function. Evaluation of minimum free energy for secondary structures of RNA can be performed through several algorithms to structure prediction [
, and it serves as a source for current methods. The present study used Mfold online software to provide easy availability to DNA and RNA folding. The main algorithm predicts a ∆G and minimum free energy for folding and must cover any specific base pair. This software was used to predict the minimum free energy of ERα RNA structure before and after PvuII SNP. In silico analysis of the present study revealed that ERα-PvuII polymorphism could affect RNA structure, but it might not affect the splicing procedure. Data indicated that the polymorphism decreased the minimum free energy of ERα RNA, thereby providing more stability for the RNA that might change the ERα gene expression [
The association between ERα-PvuII and male infertility was previously studied and the results were controversial. For instance, Meng et al. [2013] found an association of the SNP with idiopathic male infertility, whereas
reported no significant association. However, our findings are consistent with previous reports in which oestrogen has been described as promoting development of sperm. Oestrogen was traditionally considered as the main female sex hormone, but it has recently been recognised as an essential hormone in the male reproductive system [
]. By the mediation of aromatase, the enzyme that converts testosterone to oestrogen, oestrogen can be produced in testis cells such as Sertoli, Leydig, and germ cells. The effect and function of oestrogen depends on the cell in which it is produced. In germ cells, oestrogen aids development of both mature and immature sperms including the facilitation of capacitation and the acromosal reaction [
]. In Sertoli cells, it controls the spermatogenic population and supports tight junctions of cells. Finally, it is responsible for inhibiting testosterone production in Leydig cells [
]. The regulation of testicular cells by oestradiol displays both stimulatory and inhbitory features, indicating a complex pattern of dose-dependent and temporally-sensitive modulation [
]. Elevated oestrogen level is associated with male infertility because hyperoestrogenism is correlated with disorders in the adrenal cortex, testis, or medications affecting the hypothalamus-pituitary-gonadal axis [
]. Furthermore, lower semen parameters can be found in the presence of an abnormal T/E ratio [<10], and the mediation of an aromatase inhibitor regulates the ratio and improves sperm concentration, motility, and morphology [
]. The combination of oestrogen with other exogenous hormones like antiandrogens or GnRH analogues may also have an inhibitory effect on spermatogenesis. High levels of oestrogen may increase collagen synthesis, fatty degeneration of testicular connective tissues, and synthesis of glycoproteins in Sertoli and Leydig cells leading to decreased sperm count [
]. Furthermore, 17β-estradiol, the most potent naturally-occurring oestrogen in humans, efficiently inhibited apoptosis of male germ cells at a low concentration [10−9 and 10−10 mol/L] [
]. In addition, testosterone is also able to inhibit testicular apoptosis at higher levels than oestrogen [10−7 mol/L] indicating that oestrogen can be a potent factor for the survival of germ cells [
]. These findings indicate the key role of estradiol in spermatogenesis.
In somatic cells, the impact of oestrogen is mediated by at least two important oestrogen receptors (ERα and ERβ). Both phenotypes of men and animal models with ERα gene alterations indicate an important role for oestrogen in male fertility [
]. These two genes can thus be considered as suitable genetic markers that are in part responsible for spermatogenesis in human. Regulation of ER gene expression may be crucial for the human male reproduction. Guido et al. [2011] investigated the ERs expression in normal, oligoasthenoteratozoosprmic [OAT], and varicocele sperm samples. They reported that ERβ content was significantly reduced only in varicocele, whereas ERα was almost undetectable. The ERs expression might thus distinguish OAT and fertile men from those with varicocele [
Human sperm physiology: Estrogen receptor alpha [ERα] and estrogen receptor beta [ERβ] influence sperm metabolism and may be involved in the pathophysiology of varicocele–associated male infertility.
also reported that the ERα gene was highly expressed in the adult testes of rats. In addition, mRNA levels of both types of ERs were higher in round spermatids than pachytene spermatocytes indicating key roles of these receptors at haploid stages of spermatogenesis [
The present study had various limitations that should be taken into consideration. The sample size of infertile subgroups was too small and results were reported as preliminary outcomes. No evaluation was performed on gene-gene and gene-environmental interactions that might modulate the effects of the studied SNP on the male infertility. Furthermore, ER levels were not assessed by in vitro and in vivo experiments. In addition, there were insufficient studies on the African, Latino, and other ethnicities in the meta-analysis. We did not have access to some data like BMI and age to adjust results in the meta-analysis. In conclusion, the present study indicated that PvuII polymorphism in ERα gene might be a protective genetic factor against the male infertility. Further studies on various races are needed to confirm results of the present study.
Acknowledgments
The present research was supported by the research chancellor of Kashan University of Medical Sciences [Grant No.9623].
Human sperm physiology: Estrogen receptor alpha [ERα] and estrogen receptor beta [ERβ] influence sperm metabolism and may be involved in the pathophysiology of varicocele–associated male infertility.
Association of oestrogen receptor α polymorphisms and androgen receptor CAG trinucleotide repeats with male infertility: a study in 109 Greek infertile men.
Narges Mobasseri is a MSc student of anatomical sciences in the medical faculty at Kashan University of Medical Sciences, Kashan, Iran. She works at Gametogenesis Research Center of the university under the supervision of Dr. Hossein Nikzad and Dr. Mohammad Karimian. Her current focused area of research is on the genetic and epigenetic risk factors of male infertility.
Key Message
ER-PvuIIpolymorphism might be a protective genetic factor against idiopathic male infertility
Article info
Publication history
Published online: January 22, 2019
Accepted:
January 18,
2019
Received in revised form:
January 17,
2019
Received:
January 2,
2018
Declaration: Authors declared no conflict of interest.
Narges Mobasseri is a MSc student of anatomical sciences in the medical faculty at Kashan University of Medical Sciences, Kashan, Iran. She works at Gametogenesis Research Center of the university under the supervision of Dr. Hossein Nikzad and Dr. Mohammad Karimian. Her current focused area of research is on the genetic and epigenetic risk factors of male infertility.
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